SlimSAM: 0.1% Data Makes Segment Anything Slim 0.1% Data Makes Segment Anything Slim Zigeng Chen, Gongfan Fang, Xinyin Ma, Xinchao Wang Learning and Vision Lab, National University of Singapore Paper: [[Arxiv]](https://arxiv.org/abs/2312.05284) Code: [[GitHub]](https://github.com/czg1225/SlimSAM) Introduction SlimSAM is a novel SAM compression method, which efficiently reuses pre trained SAMs without the necessity for extensive retraining. This is achieved by the efficient reuse of pre trained SAMs through a unified pruning distillation framework. To enhance knowledge inheritance from the original SAM, we employ an innovative alternate slimming strategy that partitions the compression process into a progressive procedure. Diverging from prior pruning techniques, we meticulously prune and distill decoupled model structures in an alternating fashion. Furthermore, a novel label free pruning criterion is also proposed to align the pruning objective with the optimization target, thereby boosting the post distillation after pruning. SlimSAM achieves approaching performance while reducing the parameter counts to 0.9\% (5.7M) , MACs to 0.8\% (21G) , and requiring mere 0.1\% (10k) of the tr…
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